1. Field of the Invention
The present invention generally relates to a method for screening individuals for autonomic neuropathy (AN), a complication of diabetes mellitus, using cepstral encoding of heart rate (HR) signals. More specifically, the present invention relates to a method for screening individuals for AN using linear predictor-derived cepstral encoding of HR signals obtained from individuals in a supine position.
2. Related Art
Once diabetic AN becomes clinically evident, the estimated 5-year mortality is approximately 50%. Thus, the early detection of autonomic dysfunction is important for effective therapeutic intervention. Thorough diagnosis and evaluation requires a patient history and neurologic examination, that can include nerve conduction studies, needle EMG measurements, vibratory perception thresholds, and a widely accepted battery of cardiovascular function tests evaluated as the Ewing score. Specialized electronic devices also offer a means for clinical assessment. Other indicators are typically used for standard clinical assessment, including impaired vision (retinopathy), and numbness or tingling of the extremities (impaired circulation). However, it would be desirable to employ an accurate, non-invasive method for detecting AN in diabetics. Works of others in this and related areas include the following:
Patterns found in the surface electrocardiogram (ECG) have been studied extensively for the purpose of classifying abnormal waveform profiles. Automated ECG classification has been successfully implemented in clinical practice with useful results for screening, diagnosis, and monitoring. Abenstein, xe2x80x9cAlgorithms for Real Time Ambulatory ECG Monitoring,xe2x80x9d Biomed Sci Instrum., 14:73-79 (1978). Representation of the ECG pattern has included Fourier analysis, complex cepstrum, and the autoregressive (AR/ARMA) model. Murthy, et al., xe2x80x9cHomomorphic Analysis and Modeling of ECG Signals,xe2x80x9d IEEE Trans. Biomed Eng., BME-26(5):330-344 (1979), Mukhopadhyay, et al., xe2x80x9cParametric modeling of ECG Signal,xe2x80x9d Med. Biol. Eng. Comput., 4:171-173(1996). Classification approaches have included frequency analysis, template matching cluster analysis, and most recently, neural networks. Silipo, et al., xe2x80x9cNeural and Traditional Techniques in Diagnostic ECG Classification, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processingxe2x80x94Proceedings, 1:123-126 (1997).
Curcie, et al., xe2x80x9cRecognition of Individual Heart Rate Patterns with Cepstral Vectors,xe2x80x9d Biological Cybernetics, Vol. 77, pages 103-109 (1997), shows that LP-derived cepstral coefficients can be used in a weighted nearest-mean classifier to successfully discriminate HRV (heart rate variability) tachograms for individuals, and that cepstral distance can be used to discriminate between normal and cardiac patient groups.
Bannister, xe2x80x9cAutonomic Failure: A Textbook of Clinical Disorders of the Autonomic Nervous Systemxe2x80x9d, (1988), Oxford/New York, Oxford University Press, reported that, as a complication of diabetes mellitus, AN is characterized by widespread degeneration of the small nerve fibers of both the sympathetic and parasympathetic tracts.
Stys, et al., xe2x80x9cCurrent Clinical Applications of Heart Rate Variabilityxe2x80x9d, Clinical Cardiology, Vol. 21, No. 10, pages 719-724, (1998), noted that spectral analysis of heart rate variability is widely considered to be a reliable noninvasive test for quantitative clinical assessment of cardiac-autonomic regulation.
The onset of cardiac AN, such as that encountered by long term diabetics, has been shown to reflect reductions in energies in both the LF (low frequency) and HF (high frequency) spectral bands, and total power compared to controls, Freeman, et al., xe2x80x9cSpectral Analysis of Heart Rate in Diabetic Autonomic Neuropathyxe2x80x9d, Archives of Neurology Vol. 48, Pages 185-190 (1991); Weise et al., xe2x80x9cAge Related Changes in Heart Rate Power Spectra in Diabetic Man During Orthostasisxe2x80x9d, Diabetes Research and Clinical Practice, Vol. 11, pages 23-32 (1991); Bellavere, et al., xe2x80x9cPower Spectral Analysis of Heart-Rate Variations Improves Assessment of Diabetic Autonomic Neuropathy,xe2x80x9d Diabetes, Vol. 41, pages 633-640 (1991); and Howorka,et al., xe2x80x9cOptimal Parameters of Short-Term Heart Rate Spectrogram for Routine Evaluation of Diabetic Cardiovascular Autonomic Neuropathy,xe2x80x9d Journal of Autonomic Nervous System, Vol. 69, No. 2-3, pages 164-172 (1998).
The reduced change in autonomic balance during orthostatic load, measured via spectral band indices, is a clinical indicator of AN and dysautonomia. In studies, the increase in LF and LF/HF and the decrease in HF produced by tilt were found to be significantly lower in diabetics, than in controls, Pagani, et al., xe2x80x9cSpectral Analysis of Heart Rate Variability in the Assessment of Autonomic Diabetic Neuropathy,xe2x80x9d Journal of the Autonomic Nervous System, Vol. 23, No. 2, pages 143-153 (1998); Lagi, et al., xe2x80x9cPower Spectrum Analysis of Heart Rate Variations in the Early Detection of Diabetic Autonomic Neuropathy, Clinical Autonomic Research, Vol. 4, No. 5 , pages 245-248 (1994). Abnormal autonomic response to tilt in diabetics with a higher severity of cardiac AN has been shown to include a decrease in LF/HF ratio, Pagani, et al.
Signal classification techniques based on HRV indices have recently been applied to investigating HRV. Raymond et al., xe2x80x9cClassification of Heart Rate Variability in Patients with Mild Hypertension,xe2x80x9d Australasian Physical and Engineering Sciences in Medicine, Vol. 20, No. 4, pages 207-213 (1997), used spectral and time domain indices during rest and isometric handgrip as features in a Bayesian classifier to detect hypertension. Raymond et al. xe2x80x9cVisualization of Heart Rate Variability Data Using Topographic Mappings, Computers in Cardiology,xe2x80x9d (1998), further used topographic mapping of HRV log spectral distance measures to demonstrate a clustering effect corresponding to both tilt, and the presence or absence of beta blockade in healthy subjects.
Prognostic classifiers offer a potentially powerful clinical tool for identifying the onset of cardiac neuropathy. Curcie et al., Heart Rate Complexity as a Diagnostic of Autonomic Neuropathy from Insulin Dependent Diabetes Mellitus,xe2x80x9d PACE, April, (1998) showed that fractal dimension, a nonlinear measure of signal complexity, could be used on supine HR records to predict the outcome of tilt in diabetics, a possible indicator of AN, as measured by the change in the LF/HF index.
The onset of cardiac autonomic neuropathy progresses independently from somatic neuropathy, and though no connection was found between power spectrum analysis and somatic neuropathy, spectral indices are sensitive enough to detect cardiac autonomic neuropathy in diabetics where standard methods sometimes fail. Thus, an encoding and classification method capable of comprehensively featuring relevant spectral information would be useful for the early detection of diabetes-induced cardiac AN.
None of the work by others has resulted in a method for screening diabetics for AN using cepstral vector analysis of heart rate signals. The method of the present invention demonstrates that the LP-cepstral discriminant classifier is useful and reliable for quick, noninvasive assessment (screening) of neuropathy in diabetics, while avoiding the stresses to the patient that are associated with tilt table testing.
A principal object of the present invention is the provision of a method for detecting AN in patients.
Another object and advantage of the present invention is the provision of a method for detecting AN in patients using heart rate variability indications from the patient and from a control group.
Even another object and advantage of the present invention is the provision of a method for detecting AN which encodes the heart rate signals with linear-predictive cepstral encoding to create heart rate cepstral vectors (HRCV).
Still another object and advantage of the present invention is the provision of a method in which cepstral encoding of HRV provides a new identifier of the possible early onset of cardiac autonomic neuropathy.
Still a further object and advantage of the present invention is the provision of an encoding and classification method capable of comprehensively featuring relevant spectral information for the early detection of diabetes-induced cardiac AN.
An additional object and advantage of the present invention is the use of the LP-cepstral discriminant classifier for quick, noninvasive assessment (screening) of neuropathy in diabetics, while avoiding the stresses to the patient that are associated with tilt table testing.
Even an additional object and advantage of the present invention is the provision of a diabetic neuropathy screening method which is useful and reliable.
Still even an additional object and advantage of the present invention is the use LP-cepstral encoding and pattern classification as a clinical test of autonomic function in patients with known or suspected cardiac autonomic dysfunction.
Yet even an additional object and advantage of the present invention is the use of a discriminant classifier based on supine HR cepstral vectors from a population of diabetics.
The present invention relates to a method for screening individuals for AN using cepstral encoding of HR signals. Preferably, linear predictor-derived cepstral encoding of the heart rate signals are obtained from individuals in a supine position to generate a vector. The vector is compared with a classifier heart rate cepstral vector generator from a population having AN. The heart rate cepstral vector is compared with the classifier to indicate presence or absence of AN in the individual. The invention removes the need for utilizing a tilt table to diagnose AN. Approximately fifteen minutes of heart rate data from an individual in a supine position is sufficient for diagnosis.